The Slab Allocator:
An Object-Caching Kernel Memory Allocator
Jeff Bonwick
Sun Microsystems
Abstract
This paper presents a comprehensive design overview of the SunOS 5.4 kernel
memory allocator. This allocator is based on a set of object-caching primi-
tives that reduce the cost of allocating complex objects by retaining their
state between uses. These same primitives prove equally effective for manag-
ing stateless memory (e.g. data pages and temporary buffers) because they are
space-efficient and fast. The allocator's object caches respond dynamically
to global memory pressure, and employ an object-coloring scheme that improves
the system's overall cache utilization and bus balance. The allocator also
has several statistical and debugging features that can detect a wide range of
problems throughout the system.
1. Introduction
The allocation and freeing of objects are among the most common operations in
the kernel. A fast kernel memory allocator is therefore essential. However,
in many cases the cost of initializing and destroying the object exceeds the
cost of allocating and freeing memory for it. Thus, while improvements in the
allocator are beneficial, even greater gains can be achieved by caching fre-
quently used objects so that their basic structure is preserved between uses.
The paper begins with a discussion of object caching, since the interface
that this requires will shape the rest of the allocator. The next section
then describes the implementation in detail. Section 4 describes the effect
of buffer address distribution on the system's overall cache utilization and
bus balance, and shows how a simple coloring scheme can improve both. Section
5 compares the allocator's performance to several other well-known kernel
memory allocators and finds that it is generally superior in both space and
time. Finally, Section 6 describes the allocator's debugging features, which
can detect a wide variety of problems throughout the system.
2. Object Caching
Object caching is a technique for dealing with objects that are frequently
allocated and freed. The idea is to preserve the invariant portion of an
object's initial state - its constructed state - between uses, so it does not
have to be destroyed and recreated every time the object is used. For exam-
ple, an object containing a mutex only needs to have mutex_init() applied
once - the first time the object is allocated. The object can then be freed
and reallocated many times without incurring the expense of mutex_destroy()
and mutex_init() each time. An object's embedded locks, condition variables,
reference counts, lists of other objects, and read-only data all generally
qualify as constructed state.
Caching is important because the cost of constructing an object can be
significantly higher than the cost of allocating memory for it. For example,
on a SPARCstation-2 running a SunOS 5.4 development kernel, the allocator
presented here reduced the cost of allocating and freeing a stream head from
33 microseconds to 5.7 microseconds. As the table below illustrates, most of
the savings was due to object caching:
______________________________________________________________________________
Stream Head Allocation + Free Costs (usec)
______________________________________________________________________________
construction memory other
allocator + destruction allocation init.
______________________________________________________________________________
old 23.6 9.4 1.9
new 0.0 3.8 1.9
______________________________________________________________________________
Caching is particularly beneficial in a multithreaded environment, where
many of the most frequently allocated objects contain one or more embedded
locks, condition variables, and other constructible state.
The design of an object cache is straightforward:
To allocate an object:
if (there's an object in the cache)
take it (no construction required);
else {
allocate memory;
construct the object;
}
To free an object:
return it to the cache (no destruction required);
To reclaim memory from the cache:
take some objects from the cache;
destroy the objects;
free the underlying memory;
An object's constructed state must be initialized only once - when the
object is first brought into the cache. Once the cache is populated, allocat-
ing and freeing objects are fast, trivial operations.
2.1. An Example
Consider the following data structure:
struct foo {
kmutex_t foo_lock;
kcondvar_t foo_cv;
struct bar *foo_barlist;
int foo_refcnt;
};
Assume that a foo structure cannot be freed until there are no outstanding
references to it (foo_refcnt == 0) and all of its pending bar events (whatever
they are) have completed (foo_barlist == NULL). The life cycle of a dynami-
cally allocated foo would be something like this:
foo = kmem_alloc(sizeof (struct foo),
KM_SLEEP);
mutex_init(&foo->foo_lock, ...);
cv_init(&foo->foo_cv, ...);
foo->foo_refcnt = 0;
foo->foo_barlist = NULL;
use foo;
ASSERT(foo->foo_barlist == NULL);
ASSERT(foo->foo_refcnt == 0);
cv_destroy(&foo->foo_cv);
mutex_destroy(&foo->foo_lock);
kmem_free(foo);
Notice that between each use of a foo object we perform a sequence of opera-
tions that constitutes nothing more than a very expensive no-op. All of this
overhead (i.e., everything other than ``use foo'' above) can be eliminated by
object caching.
2.2. The Case for Object Caching in the Central Allocator
Of course, object caching can be implemented without any help from the central
allocator - any subsystem can have a private implementation of the algorithm
described above. However, there are several disadvantages to this approach:
(1) There is a natural tension between an object cache, which wants to keep
memory, and the rest of the system, which wants that memory back.
Privately-managed caches cannot handle this tension sensibly. They have
limited insight into the system's overall memory needs and no insight into
each other's needs. Similarly, the rest of the system has no knowledge of
the existence of these caches and hence has no way to ``pull'' memory from
them.
(2) Since private caches bypass the central allocator, they also bypass any
accounting mechanisms and debugging features that allocator may possess.
This makes the operating system more difficult to monitor and debug.
(3) Having many instances of the same solution to a common problem increases
kernel code size and maintenance costs.
Object caching requires a greater degree of cooperation between the allocator
and its clients than the standard kmem_alloc(9F)/kmem_free(9F) interface
allows. The next section develops an interface to support constructed object
caching in the central allocator.
2.3. Object Cache Interface
The interface presented here follows from two observations:
(A) Descriptions of objects (name, size, alignment, constructor, and destruc-
tor) belong in the clients - not in the central allocator. The allocator
should not just ``know'' that sizeof (struct inode) is a useful pool size,
for example. Such assumptions are brittle [Grunwald93A] and cannot
anticipate the needs of third-party device drivers, streams modules and
file systems.
(B) Memory management policies belong in the central allocator - not in its
clients. The clients just want to allocate and free objects quickly. They
shouldn't have to worry about how to manage the underlying memory effi-
ciently.
It follows from (A) that object cache creation must be client-driven and must
include a full specification of the objects:
(1) struct kmem_cache *kmem_cache_create(char *name, size_t size, int align,
void (*constructor)(void *, size_t),
void (*destructor)(void *, size_t));
Creates a cache of objects, each of size size, aligned on an align boundary.
The alignment will always be rounded up to the minimum allowable value, so
align can be zero whenever no special alignment is required. name identi-
fies the cache for statistics and debugging. constructor is a function that
constructs (that is, performs the one-time initialization of) objects in the
cache; destructor undoes this, if applicable. The constructor and destruc-
tor take a size argument so that they can support families of similar
caches, e.g. streams messages. kmem_cache_create returns an opaque descrip-
tor for accessing the cache.
Next, it follows from (B) that clients should need just two simple functions
to allocate and free objects:
(2) void *kmem_cache_alloc(struct kmem_cache *cp, int flags);
Gets an object from the cache. The object will be in its constructed state.
flags is either KM_SLEEP or KM_NOSLEEP, indicating whether it's acceptable
to wait for memory if none is currently available.
(3) void kmem_cache_free(struct kmem_cache *cp, void *buf);
Returns an object to the cache. The object must still be in its constructed
state.
Finally, if a cache is no longer needed the client can destroy it:
(4) void kmem_cache_destroy(struct kmem_cache *cp);
Destroys the cache and reclaims all associated resources. All allocated
objects must have been returned to the cache.
This interface allows us to build a flexible allocator that is ideally suited
to the needs of its clients. In this sense it is a ``custom'' allocator.
However, it does not have to be built with compile-time knowledge of its
clients as most custom allocators do [Bozman84A, Grunwald93A, Margolin71], nor
does it have to keep guessing as in the adaptive-fit methods [Bozman84B,
Leverett82, Oldehoeft85]. Rather, the object-cache interface allows clients
to specify the allocation services they need on the fly.
2.4. An Example
This example demonstrates the use of object caching for the ``foo'' objects
introduced in Section 2.1. The constructor and destructor routines are:
void
foo_constructor(void *buf, int size)
{
struct foo *foo = buf;
mutex_init(&foo->foo_lock, ...);
cv_init(&foo->foo_cv, ...);
foo->foo_refcnt = 0;
foo->foo_barlist = NULL;
}
void
foo_destructor(void *buf, int size)
{
struct foo *foo = buf;
ASSERT(foo->foo_barlist == NULL);
ASSERT(foo->foo_refcnt == 0);
cv_destroy(&foo->foo_cv);
mutex_destroy(&foo->foo_lock);
}
To create the foo cache:
foo_cache = kmem_cache_create("foo_cache", sizeof (struct foo), 0,
foo_constructor, foo_destructor);
To allocate, use, and free a foo object:
foo = kmem_cache_alloc(foo_cache, KM_SLEEP);
use foo;
kmem_cache_free(foo_cache, foo);
This makes foo allocation fast, because the allocator will usually do
nothing more than fetch an already-constructed foo from the cache.
foo_constructor and foo_destructor will be invoked only to populate and drain
the cache, respectively.
The example above illustrates a beneficial side-effect of object caching:
it reduces the instruction-cache footprint of the code that uses cached
objects by moving the rarely-executed construction and destruction code out of
the hot path.
3. Slab Allocator Implementation
This section describes the implementation of the SunOS 5.4 kernel memory allo-
cator, or ``slab allocator,'' in detail. (The name derives from one of the
allocator's main data structures, the slab. The name stuck within Sun because
it was more distinctive than ``object'' or ``cache.'' Slabs will be discussed
in Section 3.2.)
The terms object, buffer, and chunk will be used more or less inter-
changeably, depending on how we're viewing that piece of memory at the moment.
3.1. Caches
Each cache has a front end and back end which are designed to be as decoupled
as possible:
back end front end
-------- ---------
---------------
kmem_cache_grow() --> | | --> kmem_cache_alloc()
| cache |
kmem_cache_reap() | kmem | -----> | kmem |
|bufctl| |bufctl| |bufctl|
-------- -------- --------
| | |
| | |
V V V
--------------------------------------------------------
| | | | |
| buf | buf | buf |unused|
| | | | |
--------------------------------------------------------
||
3.2.2. Slab Layout for Small Objects
For objects smaller than 1/8 of a page, a slab is built by allocating a page,
placing the slab data at the end, and dividing the rest into equal-size
buffers:
------------------------ --------------------------------------
| | | | | | un- | kmem |
| buf | buf | ... | buf | buf | used | slab |
| | | | | | | data |
------------------------ --------------------------------------
||
Each buffer serves as its own bufctl while on the freelist. Only the linkage
is actually needed, since everything else is computable. These are essential
optimizations for small buffers - otherwise we would end up allocating almost
as much memory for bufctls as for the buffers themselves.
The freelist linkage resides at the end of the buffer, rather than the
beginning, to facilitate debugging. This is driven by the empirical observa-
tion that the beginning of a data structure is typically more active than the
end. If a buffer is modified after being freed, the problem is easier to
diagnose if the heap structure (freelist linkage) is still intact.
The allocator reserves an additional word for constructed objects so that
the linkage doesn't overwrite any constructed state.
3.2.3. Slab Layout for Large Objects
The above scheme is efficient for small objects, but not for large ones. It
could fit only one 2K buffer on a 4K page because of the embedded slab data.
Moreover, with large (multi-page) slabs we lose the ability to determine the
slab data address from the buffer address. Therefore, for large objects the
physical layout is identical to the logical layout. The required slab and
bufctl data structures come from their own (small-object!) caches. A per-
cache self-scaling hash table provides buffer-to-bufctl conversion.
3.3. Freelist Management
Each cache maintains a circular, doubly-linked list of all its slabs. The
slab list is partially sorted, in that the empty slabs (all buffers allocated)
come first, followed by the partial slabs (some buffers allocated, some free),
and finally the complete slabs (all buffers free, refcnt == 0). The cache's
freelist pointer points to its first non-empty slab. Each slab, in turn, has
its own freelist of available buffers. This two-level freelist structure sim-
plifies memory reclaiming. When the allocator reclaims a slab it doesn't have
to unlink each buffer from the cache's freelist - it just unlinks the slab.
3.4. Reclaiming Memory
When kmem_cache_free() sees that the slab reference count is zero, it does not
immediately reclaim the memory. Instead, it just moves the slab to the tail
of the freelist where all the complete slabs reside. This ensures that no
complete slab will be broken up unless all partial slabs have been depleted.
When the system runs low on memory it asks the allocator to liberate as
much memory as it can. The allocator obliges, but retains a 15-second working
set of recently-used slabs to prevent thrashing. Measurements indicate that
system performance is fairly insensitive to the slab working-set interval.
Presumably this is because the two extremes - zero working set (reclaim all
complete slabs on demand) and infinite working-set (never reclaim anything) -
are both reasonable, albeit suboptimal, policies.
4. Hardware Cache Effects
Modern hardware relies on good cache utilization, so it is important to design
software with cache effects in mind. For a memory allocator there are two
broad classes of cache effects to consider: the distribution of buffer
addresses and the cache footprint of the allocator itself. The latter topic
has received some attention [Chen93, Grunwald93B], but the effect of buffer
address distribution on cache utilization and bus balance has gone largely
unrecognized.
4.1. Impact of Buffer Address Distribution on Cache Utilization
The address distribution of mid-size buffers can affect the system's overall
cache utilization. In particular, power-of-two allocators - where all buffers
are 2n bytes and are 2n-byte aligned - are pessimal. (Such allocators are
common because they are easy to implement. For example, 4.4BSD and SVr4 both
employ power-of-two methods [McKusick88, Lee89].) Suppose, for example,
that every inode (~=300 bytes) is assigned a 512-byte buffer, 512-byte aligned,
and that only the first dozen fields of an inode (48 bytes) are frequently
referenced. Then the majority of inode-related memory traffic will be at
addresses between 0 and 47 modulo 512. Thus the cache lines near 512-byte
boundaries will be heavily loaded while the rest lie fallow. In effect only
9% (48/512) of the cache will be usable by inodes. Fully-associative caches
would not suffer this problem, but current hardware trends are toward simpler
rather than more complex caches.
Of course, there's nothing special about inodes. The kernel contains
many other mid-size data structures (e.g. 100-500 bytes) with the same essen-
tial qualities: there are many of them, they contain only a few heavily used
fields, and those fields are grouped together at or near the beginning of the
structure. This artifact of the way data structures evolve has not previously
been recognized as an important factor in allocator design.
4.2. Impact of Buffer Address Distribution on Bus Balance
On a machine that interleaves memory across multiple main buses, the effects
described above also have a significant impact on bus utilization. The
SPARCcenter 2000, for example, employs 256-byte interleaving across two main
buses [Cekleov92]. Continuing the example above, we see that any power-of-two
allocator maps the first half of every inode (the hot part) to bus 0 and the
second half to bus 1. Thus almost all inode-related cache misses are serviced
by bus 0. The situation is exacerbated by an inflated miss rate, since all
of the inodes are fighting over a small fraction of the cache.
These effects can be dramatic. On a SPARCcenter 2000 running LADDIS
under a SunOS 5.4 development kernel, replacing the old allocator (a power-
of-two buddy-system [Lee89]) with the slab allocator reduced bus imbalance
from 43% to just 17%. In addition, the primary cache miss rate dropped by
13%.
4.3. Slab Coloring
The slab allocator incorporates a simple coloring scheme that distributes
buffers evenly throughout the cache, resulting in excellent cache utilization
and bus balance. The concept is simple: each time a new slab is created, the
buffer addresses start at a slightly different offset (color) from the slab
base (which is always page-aligned). For example, for a cache of 200-byte
objects with 8-byte alignment, the first slab's buffers would be at addresses
0, 200, 400, ... relative to the slab base. The next slab's buffers would be
at offsets 8, 208, 408, ... and so on. The maximum slab color is determined
by the amount of unused space in the slab. In this example, assuming 4K
pages, we can fit 20 200-byte buffers in a 4096-byte slab. The buffers con-
sume 4000 bytes, the kmem_slab data consumes 32 bytes, and the remaining 64
bytes are available for coloring. Thus the maximum slab color is 64, and the
slab color sequence is 0, 8, 16, 24, 32, 40, 48, 56, 64, 0, 8, ...
One particularly nice property of this coloring scheme is that mid-size
power-of-two buffers receive the maximum amount of coloring, since they are
the worst-fitting. For example, while 128 bytes goes perfectly into 4096, it
goes near-pessimally into 4096 - 32, which is what's actually available
(because of the embedded slab data).
4.4. Arena Management
An allocator's arena management strategy determines its dynamic cache foot-
print. These strategies fall into three broad categories: sequential-fit
methods, buddy methods, and segregated-storage methods [Standish80].
A sequential-fit allocator must typically search several nodes to find a
good-fitting buffer. Such methods are, by nature, condemned to a large cache
footprint: they have to examine a significant number of nodes that are gen-
erally nowhere near each other. This causes not only cache misses, but TLB
misses as well. The coalescing stages of buddy-system allocators [Knuth68,
Lee89] have similar properties.
A segregated-storage allocator, such as the slab allocator, maintains
separate freelists for different buffer sizes. These allocators generally have
good cache locality because allocating a buffer is so simple. All the alloca-
tor has to do is determine the freelist (by computation, by table lookup, or
by having it supplied as an argument) and take a buffer from it. Freeing a
buffer is similarly straightforward. There are only a handful of pointers
to load, so the cache footprint is small.
The slab allocator has the additional advantage that for small to mid-
size buffers, most of the relevant information - the slab data, bufctls, and
buffers themselves - resides on a single page. Thus a single TLB entry covers
most of the action.
5. Performance
This section compares the performance of the slab allocator to three other
well-known kernel memory allocators:
SunOS 4.1.3, based on [Stephenson83], a sequential-fit method;
4.4BSD, based on [McKusick88], a power-of-two segregated-storage method;
SVr4, based on [Lee89], a power-of-two buddy-system method. This allocator
was employed in all previous SunOS 5.x releases.
To get a fair comparison, each of these allocators was ported into the same
SunOS 5.4 base system. This ensures that we are comparing just allocators,
not entire operating systems.
5.1. Speed Comparison
On a SPARCstation-2 the time required to allocate and free a buffer under the
various allocators is as follows:
______________________________________________________________________________
Memory Allocation + Free Costs
______________________________________________________________________________
allocator time (usec) interface
______________________________________________________________________________
slab 3.8 kmem_cache_alloc
4.4BSD 4.1 kmem_alloc
slab 4.7 kmem_alloc
SVr4 9.4 kmem_alloc
SunOS 4.1.3 25.0 kmem_alloc
______________________________________________________________________________
Note: The 4.4BSD allocator offers both functional and preprocessor macro
interfaces. These measurements are for the functional version. Non-binary
interfaces in general were not considered, since these cannot be exported to
drivers without exposing the implementation. The 4.4BSD allocator was com-
piled without KMEMSTATS defined (it's on by default) to get the fastest possi-
ble code.
A mutex_enter()/mutex_exit() pair costs 1.0 usec, so the locking required
to allocate and free a buffer imposes a lower bound of 2.0 usec. The slab and
4.4BSD allocators are both very close to this limit because they do very lit-
tle work in the common cases. The 4.4BSD implementation of kmem_alloc() is
slightly faster, since it has less accounting to do (it never reclaims
memory). The slab allocator's kmem_cache_alloc() interface is even faster,
however, because it doesn't have to determine which freelist (cache) to use -
the cache descriptor is passed as an argument to kmem_cache_alloc(). In any
event, the differences in speed between the slab and 4.4BSD allocators are
small. This is to be expected, since all segregated-storage methods are
operationally similar. Any good segregated-storage implementation should
achieve excellent performance.
The SVr4 allocator is slower than most buddy systems but still provides
reasonable, predictable speed. The SunOS 4.1.3 allocator, like most
sequential-fit methods, is comparatively slow and quite variable.
The benefits of object caching are not visible in the numbers above,
since they only measure the cost of the allocator itself. The table below
shows the effect of object caching on some of the most frequent allocations in
the SunOS 5.4 kernel (SPARCstation-2 timings, in microseconds):
______________________________________________________________________________
Effect of Object Caching
______________________________________________________________________________
allocation without with improve-
type caching caching ment
______________________________________________________________________________
allocb 8.3 6.0 1.4x
dupb 13.4 8.7 1.5x
shalloc 29.3 5.7 5.1x
allocq 40.0 10.9 3.7x
anonmap_alloc 16.3 10.1 1.6x
makepipe 126.0 98.0 1.3x
______________________________________________________________________________
All of the numbers presented in this section measure the performance of
the allocator in isolation. The allocator's effect on overall system perfor-
mance will be discussed in Section 5.3.
5.2. Memory Utilization Comparison
An allocator generally consumes more memory than its clients actually request
due to imperfect fits (internal fragmentation), unused buffers on the freelist
(external fragmentation), and the overhead of the allocator's internal data
structures. The ratio of memory requested to memory consumed is the
allocator's memory utilization. The complementary ratio is the memory wastage
or total fragmentation. Good memory utilization is essential, since the ker-
nel heap consumes physical memory.
An allocator's space efficiency is harder to characterize than its speed
because it is workload-dependent. The best we can do is to measure the various
allocators' memory utilization under a fixed set of workloads. To this end,
each allocator was subjected to the following workload sequence:
(1) System boot. This measures the system's memory utilization at the console
login prompt after rebooting.
(2) A brief spike in load, generated by the following trivial program:
fork(); fork(); fork(); fork();
fork(); fork(); fork(); fork();
fd = socket(AF_UNIX, SOCK_STREAM, 0);
sleep(60);
close(fd);
This creates 256 processes, each of which creates a socket. This causes a
temporary surge in demand for a variety of kernel data structures.
(3) Find. This is another trivial spike-generator:
find /usr -mount -exec file {} \;
(4) Kenbus. This is a standard timesharing benchmark. Kenbus generates a
large amount of concurrent activity, creating large demand for both user and
kernel memory.
Memory utilization was measured after each step. The table below summarizes
the results for a 16MB SPARCstation-1. The slab allocator significantly out-
performed the others, ending up with half the fragmentation of the nearest
competitor (results are cumulative, so the ``kenbus'' column indicates the
fragmentation after all four steps were completed):
______________________________________________________________________________
Total Fragmentation (waste)
______________________________________________________________________________
allocator boot spike find kenbus s/m
______________________________________________________________________________
slab 11% 13% 14% 14% 233
SunOS 4.1.3 7% 19% 19% 27% 210
4.4BSD 20% 43% 43% 45% 205
SVr4 23% 45% 45% 46% 199
______________________________________________________________________________
The last column shows the kenbus results, which measure peak throughput
in units of scripts executed per minute (s/m). Kenbus performance is pri-
marily memory-limited on this 16MB system, which is why the SunOS 4.1.3 allo-
cator achieved better results than the 4.4BSD allocator despite being signifi-
cantly slower. The slab allocator delivered the best performance by an 11%
margin because it is both fast and space-efficient.
To get a handle on real-life performance the author used each of these
allocators for a week on his personal desktop machine, a 32MB SPARCstation-2.
This machine is primarily used for reading e-mail, running simple commands and
scripts, and connecting to test machines and compute servers. The results of
this obviously non-controlled experiment were:
______________________________________________________________________________
Effect of One Week of Light Desktop Use
______________________________________________________________________________
kernel fragmen-
allocator heap tation
______________________________________________________________________________
slab 6.0 MB 9%
SunOS 4.1.3 6.7 MB 17%
SVr4 8.5 MB 35%
4.4BSD 9.0 MB 38%
______________________________________________________________________________
These numbers are consistent with the results from the synthetic workload
described above. In both cases, the slab allocator generates about half the
fragmentation of SunOS 4.1.3, which in turn generates about half the fragmen-
tation of SVr4 and 4.4BSD.
5.3. Overall System Performance
The kernel memory allocator affects overall system performance in a variety of
ways. In previous sections we considered the effects of several individual
factors: object caching, hardware cache and bus effects, speed, and memory
utilization. We now turn to the most important metric: the bottom-line per-
formance of interesting workloads. In SunOS 5.4 the SVr4-based allocator was
replaced by the slab allocator described here. The table below shows the net
performance improvement in several key areas.
______________________________________________________________________________
System Performance Improvement
with Slab Allocator
______________________________________________________________________________
workload gain what it measures
______________________________________________________________________________
DeskBench 12% window system
kenbus 17% timesharing
TPC-B 4% database
LADDIS 3% NFS service
parallel make 5% parallel compilation
terminal server 5% many-user typing
______________________________________________________________________________
Notes:
(1) DeskBench and kenbus are both memory-bound in 16MB, so most of the improve-
ment here is due to the slab allocator's space efficiency.
(2) The TPC-B workload causes very little kernel memory allocation, so the
allocator's speed is not a significant factor here. The test was run on a
large server with enough memory that it never paged (under either alloca-
tor), so space efficiency is not a factor either. The 4% performance
improvement is due solely to better cache utilization (5% fewer primary
cache misses, 2% fewer secondary cache misses).
(3) Parallel make was run on a large server that never paged. This workload
generates a lot of allocator traffic, so the improvement here is attribut-
able to the slab allocator's speed, object caching, and the system's lower
overall cache miss rate (5% fewer primary cache misses, 4% fewer secondary
cache misses).
(4) Terminal server was also run on a large server that never paged. This
benchmark spent 25% of its time in the kernel with the old allocator, versus
20% with the new allocator. Thus, the 5% bottom-line improvement is due to
a 20% reduction in kernel time.
6. Debugging Features
Programming errors that corrupt the kernel heap - such as modifying freed
memory, freeing a buffer twice, freeing an uninitialized pointer, or writing
beyond the end of a buffer - are often difficult to debug. Fortunately, a
thoroughly instrumented kernel memory allocator can detect many of these prob-
lems.
This section describes the debugging features of the slab allocator.
These features can be enabled in any SunOS 5.4 kernel (not just special debug-
ging versions) by booting under kadb (the kernel debugger) and setting the
appropriate flags. (The availability of these debugging features adds no cost
to most allocations. The per-cache flag word that indicates whether a hash
table is present - i.e., whether the cache's objects are larger than 1/8 of a
page - also contains the debugging flags. A single test checks all of these
flags simultaneously, so the common case (small objects, no debugging) is
unaffected.) When the allocator detects a problem, it provides
detailed diagnostic information on the system console.
6.1. Auditing
In audit mode the allocator records its activity in a circular transaction
log. It stores this information in an extended version of the bufctl struc-
ture that includes the thread pointer, hi-res timestamp, and stack trace of
the transaction. When corruption is detected by any of the other methods, the
previous owners of the affected buffer (the likely suspects) can be deter-
mined.
6.2. Freed-Address Verification
The buffer-to-bufctl hash table employed by large-object caches can be used as
a debugging feature: if the hash lookup in kmem_cache_free() fails, then the
caller must be attempting to free a bogus address. The allocator can verify
all freed addresses by changing the ``large object'' threshold to zero.
6.3. Detecting Use of Freed Memory
When an object is freed, the allocator applies its destructor and fills it
with the pattern 0xdeadbeef. The next time that object is allocated, the
allocator verifies that it still contains the deadbeef pattern. It then fills
the object with 0xbaddcafe and applies its constructor. The deadbeef and
baddcafe patterns are chosen to be readily human-recognizable in a debugging
session. They represent freed memory and uninitialized data, respectively.
6.4. Redzone Checking
Redzone checking detects writes past the end of a buffer. The allocator
checks for redzone violations by adding a guard word to the end of each buffer
and verifying that it is unmodified when the buffer is freed.
6.5. Synchronous Unmapping
Normally, the slab working-set algorithm retains complete slabs for a while.
In synchronous-unmapping mode the allocator destroys complete slabs immedi-
ately. kmem_slab_destroy() returns the underlying memory to the back-end page
supplier, which unmaps the page(s). Any subsequent reference to any object in
that slab will cause a kernel data fault.
6.6. Page-per-buffer Mode
In page-per-buffer mode each buffer is given an entire page (or pages) so that
every buffer can be unmapped when it is freed. The slab allocator implements
this by increasing the alignment for all caches to the system page size.
(This feature requires an obscene amount of physical memory.)
6.7. Leak Detection
The timestamps provided by auditing make it easy to implement a crude kernel
memory leak detector at user level. All the user-level program has to do is
periodically scan the arena (via /dev/kmem), looking for the appearance of
new, persistent allocations. For example, any buffer that was allocated an
hour ago and is still allocated now is a possible leak.
6.8. An Example
This example illustrates the slab allocator's response to modification of a
free snode:
kernel memory allocator: buffer modified after being freed
modification occurred at offset 0x18 (0xdeadbeef replaced by 0x34)
buffer=ff8eea20 bufctl=ff8efef0 cache: snode_cache
previous transactions on buffer ff8eea20:
thread=ff8b93a0 time=T-0.000089 slab=ff8ca8c0 cache: snode_cache
kmem_cache_alloc+f8
specvp+48
ufs_lookup+148
lookuppn+3ac
lookupname+28
vn_open+a4
copen+6c
syscall+3e8
thread=ff8b94c0 time=T-1.830247 slab=ff8ca8c0 cache: snode_cache
kmem_cache_free+128
spec_inactive+208
closef+94
syscall+3e8
(transaction log continues at ff31f410)
kadb[0]:
Other errors are handled similarly. These features have proven helpful in
debugging a wide range of problems during SunOS 5.4 development.
7. Future Directions
7.1. Managing Other Types of Memory
The slab allocator gets its pages from segkmem via the routines
kmem_getpages() and kmem_freepages(); it assumes nothing about the underlying
segment driver, resource maps, translation setup, etc. Since the allocator
respects this firewall, it would be trivial to plug in alternate back-end page
suppliers. The ``getpages'' and ``freepages'' routines could be supplied as
additional arguments to kmem_cache_create(). This would allow us to manage
multiple types of memory (e.g. normal kernel memory, device memory, pageable
kernel memory, NVRAM, etc.) with a single allocator.
7.2. Per-Processor Memory Allocation
The per-processor allocation techniques of McKenney and Slingwine [McKenney93]
would fit nicely on top of the slab allocator. They define a four-layer allo-
cation hierarchy of decreasing speed and locality: per-CPU, global, coalesce-
to-page, and coalesce-to-VM-block. The latter three correspond closely to the
slab allocator's front-end, back-end, and page-supplier layers, respectively.
Even in the absence of lock contention, small per-processor freelists could
improve performance by eliminating locking costs and reducing invalidation
traffic.
7.3. User-level Applications
The slab allocator could also be used as a user-level memory allocator. The
back-end page supplier could be mmap(2) or sbrk(2).
8. Conclusions
The slab allocator is a simple, fast, and space-efficient kernel memory allo-
cator. The object-cache interface upon which it is based reduces the cost of
allocating and freeing complex objects and enables the allocator to segregate
objects by size and lifetime distribution. Slabs take advantage of object
size and lifetime segregation to reduce internal and external fragmentation,
respectively. Slabs also simplify reclaiming by using a simple reference
count instead of coalescing. The slab allocator establishes a push/pull rela-
tionship between its clients and the VM system, eliminating the need for arbi-
trary limits or watermarks to govern reclaiming. The allocator's coloring
scheme distributes buffers evenly throughout the cache, improving the system's
overall cache utilization and bus balance. In several important areas, the
slab allocator provides measurably better system performance.
Acknowledgements
Neal Nuckolls first suggested that the allocator should retain an object's
state between uses, as our old streams allocator did (it now uses the slab
allocator directly). Steve Kleiman suggested using VM pressure to regulate
reclaiming. Gordon Irlam pointed out the negative effects of power-of-two
alignment on cache utilization; Adrian Cockcroft hypothesized that this might
explain the bus imbalance we were seeing on some machines (it did).
I'd like to thank Cathy Bonwick, Roger Faulkner, Steve Kleiman, Tim Mars-
land, Rob Pike, Andy Roach, Bill Shannon, and Jim Voll for their thoughtful
comments on draft versions of this paper. Thanks also to David Robinson,
Chaitanya Tikku, and Jim Voll for providing some of the measurements, and to
Ashok Singhal for providing the tools to measure cache and bus activity.
Most of all, I thank Cathy for putting up with me (and without me) during
this project.
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Author Information
Jeff Bonwick is a kernel hacker at Sun. He likes to rip out big, slow, old
code and replace it with small, fast, new code. He still can't believe he
gets paid for this. The author received a B.S. in Mathematics from the
University of Delaware (1987) and an M.S. in Statistics from Stanford (1990).
He can be flamed electronically at bonwick@eng.sun.com.